Abstract

Purpose – The purpose of this paper is to explore the link between R&D internationalization and finance decisions by providing survey evidence on the funding sources of R&D in light of recent perceptions of MNEs' decentralized knowledge-related competitiveness. Design/methodology/approach – The study draws upon a survey of 83 decentralized R&D units located in countries outside the EU core of mature economies. Funding sources are classified as MNE-internal and external. Using simulation-based models, six ordered probit regression models were run with each funding source forming the dependent variable. Findings – There are three significant findings. First, decentralized R&D funding relies mainly on MNE-internal sources. Second, decentralized R&D funding patterns reflect on the interdependencies within an overall MNE R&D strategy that is articulated through varied laboratory roles. Third, while the strategy-finance interaction is confirmed, the prevalence of parent funding is challenged. Instead, evidence recorded here suggests support for the decentralized, “subsidiary-focused” perspective that has recently regained a considerable momentum in the literature. Research limitations/implications – Notwithstanding that this study reinforces the interaction between strategic decisions and funding patterns, more work is required to identify the effects of other aspects of strategy on the funding sources of decentralized R&D activity. However, it is seemingly the first study that investigates MNEs' R&D financial decisions in economies outside the EU core, incorporating also a wide array of external funding sources. Practical implications – Funding choices for decentralized R&D labs should be contingent upon their unique contribution to the competitive evolution of the MNE group. Further, R&D activity seems to leverage the autonomy of subsidiary managers. Originality/value – This research is one of the very few empirical studies providing evidence on the funding sources of decentralized R&D laboratories, and suggests possible predictors that have not been put forward hitherto.

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